Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present \emph{RouteNet-Erlang}, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios.
@article{arxiv.2202.13956,
title = {RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation},
author = {Miquel Ferriol-Galmés and Krzysztof Rusek and José Suárez-Varela and Shihan Xiao and Xiangle Cheng and Pere Barlet-Ros and Albert Cabellos-Aparicio},
journal= {arXiv preprint arXiv:2202.13956},
year = {2022}
}
Comments
arXiv admin note: text overlap with arXiv:2110.01261